# Sepal width and Petal width # for the Setos and Versicolor iris = sklearn.datasets.load_iris() # prepocessing data so be used with the PLA model iris_data = pd.DataFrame(iris.data, columns=iris.feature_names) iris_data['Class'] = iris.target # I will be using the first 100 data points from iris_data iris_test = shuffle_data(iris_data, 100) # I will input iris_test (shuffled data) and columns 0,2,4,5 x_train, y_train, x_test, y_test = train_test_data_split(iris_test, [0, 1, 3, 5]) iris_model = Perceptron() w, sse = iris_model.train(x_train, y_train) y_pred = iris_model.test(x_test, y_test) print("The accuracy of the model is: ", accuracy_score(y_test, y_pred)) print("The final weights are: ", w) print("SSE Cost") print(sse) # Simple scatter plot that shows the linearly seperable data. plt.scatter(x_train[:,1], x_train[:,2], c = y_train,alpha=0.8) plt.title("Perceptron") plot_decision_regions(x_train[:, 1:], y_train.astype(np.integer), clf=iris_model) plt.title('Perceptron Model') plt.xlabel('Sepal Width [cm]') plt.ylabel('Petal Width [cm]') plt.show()
from sklearn.feature_selection import chi2 from sklearn.linear_model import Perceptron my_data=genfromtxt('table.csv',delimiter=',') train_set=my_data[:,1:5]; test_set=my_data[:,6]; inter_test=np.ones(3473,1) count=2000; print inter_test.shape clf=Perceptron(); clf.fit(train_set,inter_test); clf.test(test_Set); pk_normal =write(test_set)